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Kullmann A, Akberali F, Van Gompel JJ, McGovern RA, Marsh WR, Kridner D, Diaz-Botia CA, Park MC. Implantation accuracy of novel polyimide stereotactic electroencephalographic depth electrodes-a human cadaveric study. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1320762. [PMID: 38456122 PMCID: PMC10917981 DOI: 10.3389/fmedt.2024.1320762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024] Open
Abstract
Introduction Stereoelectroencephalography (sEEG) is a minimally invasive procedure that uses depth electrodes stereotactically implanted into brain structures to map the origin and propagation of seizures in epileptic patients. Implantation accuracy of sEEG electrodes plays a critical role in the safety and efficacy of the procedure. This study used human cadaver heads, simulating clinical practice, to evaluate (1) neurosurgeon's ability to implant a new thin-film polyimide sEEG electrode according to the instructions for use (IFU), and (2) implantation accuracy. Methods Four neurosurgeons (users) implanted 24 sEEG electrodes into two cadaver heads with the aid of the ROSA robotic system. Usability was evaluated using a questionnaire that assessed completion of all procedure steps per IFU and user errors. For implantation accuracy evaluation, planned electrode trajectories were compared with post-implantation trajectories after fusion of pre- and postoperative computer tomography (CT) images. Implantation accuracy was quantified using the Euclidean distance for entry point error (EPE) and target point error (TPE). Results All sEEG electrodes were successfully placed following the IFU without user errors, and post-implant survey of users showed favorable handling characteristics. The EPE was 1.28 ± 0.86 mm and TPE was 1.61 ± 0.89 mm. Long trajectories (>50 mm) had significantly larger EPEs and TPEs than short trajectories (<50 mm), and no differences were found between orthogonal and oblique trajectories. Accuracies were similar or superior to those reported in the literature when using similar experimental conditions, and in the same range as those reported in patients. Discussion The results demonstrate that newly developed polyimide sEEG electrodes can be implanted as accurately as similar devices in the marker without user errors when following the IFU in a simulated clinical environment. The human cadaver ex-vivo test system provided a realistic test system, owing to the size, anatomy and similarity of tissue composition to that of the live human brain.
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Affiliation(s)
- Aura Kullmann
- NeuroOne Medical Technologies, Eden Prairie, MN, United States
| | | | | | - Robert A. McGovern
- Department of Neurosurgery, University of Minnesota Medical Center, Minneapolis, MN, United States
| | - W. Richard Marsh
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Debra Kridner
- NeuroOne Medical Technologies, Eden Prairie, MN, United States
| | | | - Michael C. Park
- Department of Neurosurgery, University of Minnesota Medical Center, Minneapolis, MN, United States
- Department of Neurology, University of Minnesota Medical Center, Minneapolis, MN, United States
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Li P, Zhou Y, Zhang Q, Yang Y, Wang M, Zhu R, Li H, Gu S, Zhao R. Frameless robot-assisted stereoelectroencephalography-guided radiofrequency: methodology, results, complications and stereotactic application accuracy in pediatric hypothalamic hamartomas. Front Neurol 2023; 14:1259171. [PMID: 37928157 PMCID: PMC10621047 DOI: 10.3389/fneur.2023.1259171] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Objective We aimed to investigate the methodology, results, complications and stereotactic application accuracy of electrode implantation and its explanatory variables in stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-RFTC) for pediatric hypothalamic hamartoma. Methods Children with hypothalamic hamartoma who underwent robot-assisted SEEG-RFTC between December 2017 and November 2021 were retrospectively analyzed. The methodology, seizure outcome, complications, in vivo accuracy of electrode implantation and its explanatory variables were analyzed. Results A total of 161 electrodes were implanted in 28 patients with 30 surgeries. Nine electrodes not following the planned trajectories due to intraoperative replanning were excluded, and the entry point and target point errors of 152 electrodes were statistically analyzed. The median entry point error was 0.87 mm (interquartile range, 0.50-1.41 mm), and the median target point error was 2.74 mm (interquartile range, 2.01-3.63 mm). Multifactor analysis showed that whether the electrode was bent (b = 2.16, p < 0.001), the length of the intracranial electrode (b = 0.02, p = 0.049), and the entry point error (b = 0.337, p = 0.017) had statistically significant effects on the target error. During follow-up (mean duration 31 months), 27 of 30 (90%) procedures were seizure-free. The implantation-related complication rate was 2.6% (4/152), and the major complication rate in all procedures was 6.7% (2/30). Conclusion Robot-assisted SEEG-RFTC is a safe, effective and accurate procedure for pediatric hypothalamic hamartoma. Explanatory variables significantly associated with the target point localization error at multivariate analysis include whether the intracranial electrode is bent, the intracranial electrode length and the entry point error.
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Affiliation(s)
- Ping Li
- Department of Neurosurgery, Hainan Women and Children's Medical Center, Haikou, China
| | - Yuanfeng Zhou
- Department of Neurology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
| | - Qin Zhang
- Department of Neurosurgery, Hainan Women and Children's Medical Center, Haikou, China
| | - Yuantao Yang
- Department of Neurosurgery, Hainan Women and Children's Medical Center, Haikou, China
| | - Min Wang
- Department of Neurosurgery, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
| | - Renqing Zhu
- Department of Neurosurgery, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
| | - Hao Li
- Department of Neurosurgery, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
| | - Shuo Gu
- Department of Neurosurgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Rui Zhao
- Department of Neurosurgery, Hainan Women and Children's Medical Center, Haikou, China
- Department of Neurosurgery, Children’s Hospital of Shanghai, Shanghai, China
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Mamelak AN. In Reply: Placement of Stereotactic Electroencephalography Depth Electrodes Using the Stealth Autoguide Robotic System: Technical Methods and Initial Results. Oper Neurosurg (Hagerstown) 2022; 23:e218-e219. [PMID: 35972124 DOI: 10.1227/ons.0000000000000352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023] Open
Affiliation(s)
- Adam N Mamelak
- Department of Neurological Surgery, Functional and Epilepsy Surgery, Cedars-Sinai Medical Center, Los Angeles, California, USA
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Dasgupta D, Miserocchi A, McEvoy AW, Duncan JS. Previous, current, and future stereotactic EEG techniques for localising epileptic foci. Expert Rev Med Devices 2022; 19:571-580. [PMID: 36003028 DOI: 10.1080/17434440.2022.2114830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Drug-resistant focal epilepsy presents a significant morbidity burden globally, and epilepsy surgery has been shown to be an effective treatment modality. Therefore, accurate identification of the epileptogenic zone for surgery is crucial, and in those with unclear noninvasive data, stereoencephalography is required. AREAS COVERED This review covers the history and current practices in the field of intracranial EEG, particularly analyzing how stereotactic image-guidance, robot-assisted navigation, and improved imaging techniques have increased the accuracy, scope, and use of SEEG globally. EXPERT OPINION We provide a perspective on the future directions in the field, reviewing improvements in predicting electrode bending, image acquisition, machine learning and artificial intelligence, advances in surgical planning and visualization software and hardware. We also see the development of EEG analysis tools based on machine learning algorithms that are likely to work synergistically with neurophysiology experts and improve the efficiency of EEG and SEEG analysis and 3D visualization. Improving computer-assisted planning to minimize manual input from the surgeon, and seamless integration into an ergonomic and adaptive operating theater, incorporating hybrid microscopes, virtual and augmented reality is likely to be a significant area of improvement in the near future.
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Affiliation(s)
- Debayan Dasgupta
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.,Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Anna Miserocchi
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Andrew W McEvoy
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
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Kogias E, Altenmüller DM, Karakolios K, Egger K, Coenen VA, Schulze-Bonhage A, Reinacher PC. Electrode placement for SEEG: Combining stereotactic technique with latest generation planning software for intraoperative visualization and postoperative evaluation of accuracy and accuracy predictors. Clin Neurol Neurosurg 2022; 213:107137. [DOI: 10.1016/j.clineuro.2022.107137] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 11/24/2022]
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Comparison of robotic and manual implantation of intracerebral electrodes: a single-centre, single-blinded, randomised controlled trial. Sci Rep 2021; 11:17127. [PMID: 34429470 PMCID: PMC8385074 DOI: 10.1038/s41598-021-96662-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 08/06/2021] [Indexed: 01/21/2023] Open
Abstract
There has been a significant rise in robotic trajectory guidance devices that have been utilised for stereotactic neurosurgical procedures. These devices have significant costs and associated learning curves. Previous studies reporting devices usage have not undertaken prospective parallel-group comparisons before their introduction, so the comparative differences are unknown. We study the difference in stereoelectroencephalography electrode implantation time between a robotic trajectory guidance device (iSYS1) and manual frameless implantation (PAD) in patients with drug-refractory focal epilepsy through a single-blinded randomised control parallel-group investigation of SEEG electrode implantation, concordant with CONSORT statement. Thirty-two patients (18 male) completed the trial. The iSYS1 returned significantly shorter median operative time for intracranial bolt insertion, 6.36 min (95% CI 5.72–7.07) versus 9.06 min (95% CI 8.16–10.06), p = 0.0001. The PAD group had a better median target point accuracy 1.58 mm (95% CI 1.38–1.82) versus 1.16 mm (95% CI 1.01–1.33), p = 0.004. The mean electrode implantation angle error was 2.13° for the iSYS1 group and 1.71° for the PAD groups (p = 0.023). There was no statistically significant difference for any other outcome. Health policy and hospital commissioners should consider these differences in the context of the opportunity cost of introducing robotic devices. Trial registration: ISRCTN17209025 (https://doi.org/10.1186/ISRCTN17209025).
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Vakharia VN, Sparks RE, Granados A, Miserocchi A, McEvoy AW, Ourselin S, Duncan JS. Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning. Front Neurol 2020; 11:706. [PMID: 32765411 PMCID: PMC7380116 DOI: 10.3389/fneur.2020.00706] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 06/10/2020] [Indexed: 11/17/2022] Open
Abstract
Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using “spatial priors” derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices. Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically. Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements. Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.
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Affiliation(s)
- Vejay N Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom.,National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Chalfont Centre for Epilepsy, London, United Kingdom
| | - Rachel E Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alejandro Granados
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom.,National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Chalfont Centre for Epilepsy, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom.,National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Chalfont Centre for Epilepsy, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom.,National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Chalfont Centre for Epilepsy, London, United Kingdom
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Granados A, Rodionov R, Vakharia V, McEvoy AW, Miserocchi A, O'Keeffe AG, Duncan JS, Sparks R, Ourselin S. Automated computation and analysis of accuracy metrics in stereoencephalography. J Neurosci Methods 2020; 340:108710. [PMID: 32339522 PMCID: PMC7456795 DOI: 10.1016/j.jneumeth.2020.108710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 11/25/2022]
Abstract
Automatic computation of SEEG accuracy metrics agree with those done manually. The choice of image to generate a scalp model has an effect on entry point metrics. Metrics have the lowest mean and variability when using an electrode bolt axis. Lateral shift deviation should include a measure of insertion depth error.
Background Implantation accuracy of electrodes during neurosurgical interventions is necessary to ensure safety and efficacy. Typically, metrics are computed by visual inspection which is tedious, prone to inter-/intra-observer variation, and difficult to replicate across sites. New Method We propose an automated approach for computing implantation metrics and investigate potential sources of error. We focus on accuracy metrics commonly reported in the literature to validate our approach against metrics computed manually including entry point (EP) and target point (TP) localisation errors and angle differences between planned and implanted trajectories in 15 patients with a total of 158 stereoelectroencephalography (SEEG) electrodes. We evaluate the effect of line-of-best-fit approaches, EP definition and lateral versus Euclidean distance on metrics to provide recommendations for reporting implantation accuracy metrics. Results We found no bias between manual and automated approaches for calculating accuracy metrics with limits of agreement of ±1 mm and ±1°. Automated metrics are robust to sources of errors including registration and electrode bending. We observe the highest error in EP deviations of μ = 0.25 mm when the post-implantation CT is used to define the point of entry. Comparison with Existing Method(s) We found no reports of automated approaches for quality assessment of SEEG electrode implantation. Neither the choice of metrics nor the possible errors that could occur have been investigated previously. Conclusions Our automated approach is useful to avoid human errors, unintentional bias and variation that may be introduced when manually computing metrics. Our work is relevant and timely to facilitate comparisons of studies reporting implantation accuracy.
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Affiliation(s)
- Alejandro Granados
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
| | - Roman Rodionov
- National Hospital of Neurology and Neurosurgery, London, UK
| | - Vejay Vakharia
- National Hospital of Neurology and Neurosurgery, London, UK
| | | | | | | | - John S Duncan
- National Hospital of Neurology and Neurosurgery, London, UK; Dept of Clin and Experim Epilepsy, UCL Queen Square, Inst of Neurol, UK
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Sébastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
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